A comprehensive forecasting process involves the integration of spatial load forecasting and the network information system. The most important forecasting background information includes electricity consumption, zoning, structure and electricity network data. In areas where a substantial amount of background information is available, forecasting would then be based on zoning and current electricity consumption data. The integration requires importing zoning information to the network information system and developing analyzing tools to group electric consumption data. As a result, this improves the efficiency of the network information system.

Electricity network companies are a part of society and their operational environment is rapidly changing. These changes need to be accurately predicted well in advance due to the electricity distribution network lengthy planning periods and lead times. Additionally, this thesis focuses on the research of the three most significant changes concerning spatial load forecasting: electric cars, heat pumps and decentralized production.